Density-preserving Deep Point Cloud Compression
- URL: http://arxiv.org/abs/2204.12684v1
- Date: Wed, 27 Apr 2022 03:42:15 GMT
- Title: Density-preserving Deep Point Cloud Compression
- Authors: Yun He, Xinlin Ren, Danhang Tang, Yinda Zhang, Xiangyang Xue, Yanwei
Fu
- Abstract summary: We propose a novel deep point cloud compression method that preserves local density information.
Our method works in an auto-encoder fashion: the encoder downsamples the points and learns point-wise features, while the decoder upsamples the points using these features.
- Score: 72.0703956923403
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Local density of point clouds is crucial for representing local details, but
has been overlooked by existing point cloud compression methods. To address
this, we propose a novel deep point cloud compression method that preserves
local density information. Our method works in an auto-encoder fashion: the
encoder downsamples the points and learns point-wise features, while the
decoder upsamples the points using these features. Specifically, we propose to
encode local geometry and density with three embeddings: density embedding,
local position embedding and ancestor embedding. During the decoding, we
explicitly predict the upsampling factor for each point, and the directions and
scales of the upsampled points. To mitigate the clustered points issue in
existing methods, we design a novel sub-point convolution layer, and an
upsampling block with adaptive scale. Furthermore, our method can also compress
point-wise attributes, such as normal. Extensive qualitative and quantitative
results on SemanticKITTI and ShapeNet demonstrate that our method achieves the
state-of-the-art rate-distortion trade-off.
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